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Record W2104626449 · doi:10.1109/ccece.2011.6030683

An FPGA-based 77 GHZ MEMS radar signal processing system for automotive collision avoidance

2011· article· en· W2104626449 on OpenAlexaff
Sundeep Lal, Sazzadur Chowdhury

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRadarField-programmable gate arrayContinuous-wave radarSIGNAL (programming language)Microelectromechanical systemsComputer scienceSignal processingPositioning systemElectronic engineeringEngineeringRadar imagingAcousticsComputer hardwareTelecommunicationsPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

An FPGA implemented signal processing algorithm to determine the range and velocity of targets using a MEMS based FMCW 77 GHz long range automotive radar has been presented. The MEMS radar uses two MEMS SP3T RF switches, two microfabricated Rotman lenses and two microstrip antenna arrays in addition to other microelectronic components to realize a directional beam that can scan the target area with a 6.8 ms cycle time. By sequencing the FMCW signal through the 3 beamport of the Rotman lens, the beam can be steered by ±4 degrees. The developed signal processing and control algorithm has been implanted in a Xilinx Virtex 5 FPGA. A worst case range accuracy of ±0.25m and velocity accuracy of ±0.83 m/s has been achieved which is better than the state-of-the art Bosch LRR3 radar sensor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.225
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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